With the rise of Large Language Models (LLMs) like LLaMA and Falcon, powered by Lit-GPT from Lightning AI, creating a chatbot has never been easier. In this guide, we’ll walk you through the steps to set up an Inference API for these LLMs, and we’ll discuss how to launch your very own chatbot application!

Step 1: Installation

First things first, you need to get your environment prepared. Here’s how you can install the necessary packages:

• Install the LLM Inference package via pip:

pip install llm-inference

• To install from the main branch, run:

pip install git+https://github.com/aniketmaurya/llm-inference.git@main

• Additionally, you’ll need to manually install Lit-GPT and set up the model weights:

pip install lit_gpt@git+https://github.com/aniketmaurya/install-lit-gpt.git@install

Step 2: Setting Up Inference

Now that your environment is ready, it’s time to prepare for inference. This step is where the magic happens! Conceptually, think of setting up inference like preparing a chef’s kitchen: you gather ingredients and tools before whipping up a delicious meal. In this case, the “ingredients” are the model weights and configurations needed to make predictions.

To set up inference, execute the following:

python
from llm_inference import LLMInference, prepare_weights

path = prepare_weights('EleutherAI/pythia-70m')
model = LLMInference(checkpoint_dir=path)
print(model("New York is located in"))

In this snippet, you prepare the weights for a specific model, and then you initialize the LLMInference object. The last line sends a query to the model to receive a response about New York’s location.

Step 3: Using the Chatbot

Next, we’ll set up your chatbot. This is similar to creating a conversation flow in a game where players interact with the characters. Here’s how you can set it up:

python
from llm_chain import LitGPTConversationChain, LitGPTLLM
from llm_inference import prepare_weights

path = str(prepare_weights('meta-llama/Llama-2-7b-chat-hf'))
llm = LitGPTLLM(checkpoint_dir=path, quantize=bnb.nf4)  # 7GB GPU memory
bot = LitGPTConversationChain.from_llm(llm=llm, prompt=llama2_prompt_template)
print(bot.send("hi, what is the capital of France?"))

This code initializes the chatbot by preparing the required weights, creating an LLM instance with specific memory usage, and finally setting up a conversation chain where you can engage with the bot. It’s our very own conversational companion!

Step 4: Launch the Chatbot App

Finally, let’s get that chatbot up and running! Follow these steps:

1. Download the weights:

python
from llm_inference import prepare_weights
path = prepare_weights('meta-llama/Llama-2-7b-chat-hf')

2. Launch the Gradio App:

python examples/chatbot/gradio_demo.py

Your chatbot should now be live! You can interact with it directly from the Gradio interface.

Troubleshooting

As you embark on your journey of creating and deploying this AI-powered chatbot, you may encounter some bumps along the way. Here are some troubleshooting tips:

• If the model is not loading, check your internet connection and ensure you have the correct model weights.
• If you encounter memory issues, try using a model that requires less GPU memory, or consider upgrading your hardware.
• In case of installation errors, ensure that your Python environment is up to date and that all dependencies are correctly installed.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.